Python Long-Term Stock Strategy
Budget: ₹600 – ₹1,500 INR
I’m looking to have a fully automated, long-term stock-trading strategy developed end-to-end in Python. The system should scan listed equities, generate signals, size positions, and place orders without manual intervention.
The approach is algorithmic: I’m open to a mix of quantitative fundamentals, momentum factors, and risk filters, provided the logic is transparent and backed by data. Please write clean, modular code that relies on mainstream libraries such as pandas, NumPy, TA-Lib (or your preferred technical stack), and connects to a reliable market-data/API source—Alpaca, Interactive Brokers, or similar—so I can switch providers later without a rewrite.
Back-testing on at least ten years of daily data is essential, with performance metrics (CAGR, max drawdown, Sharpe) clearly reported and plotted. I also want walk-forward testing to confirm robustness before any live deployment.
Deliverables:
• Fully commented Python scripts or notebook(s) ready to run
• A concise setup guide covering data/API credentials and required packages
• Back-test report with plots and metrics in PDF or HTML
• Optional: a lightweight dashboard or CLI that shows current signals, open positions, and equity curve
I will validate the project once the strategy reproduces the reported results on my machine and the code style allows easy parameter tweaks for future research.
The approach is algorithmic: I’m open to a mix of quantitative fundamentals, momentum factors, and risk filters, provided the logic is transparent and backed by data. Please write clean, modular code that relies on mainstream libraries such as pandas, NumPy, TA-Lib (or your preferred technical stack), and connects to a reliable market-data/API source—Alpaca, Interactive Brokers, or similar—so I can switch providers later without a rewrite.
Back-testing on at least ten years of daily data is essential, with performance metrics (CAGR, max drawdown, Sharpe) clearly reported and plotted. I also want walk-forward testing to confirm robustness before any live deployment.
Deliverables:
• Fully commented Python scripts or notebook(s) ready to run
• A concise setup guide covering data/API credentials and required packages
• Back-test report with plots and metrics in PDF or HTML
• Optional: a lightweight dashboard or CLI that shows current signals, open positions, and equity curve
I will validate the project once the strategy reproduces the reported results on my machine and the code style allows easy parameter tweaks for future research.